University of Oulu

K. Umebayashi, Y. Kasahara, H. Iwata, A. Al-Tahmeesschi and J. Vartiainen, "Spectrum Occupancy Prediction based on adaptive Recurrent Neural Networks," 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, United Kingdom, 2023, pp. 1-6, doi: 10.1109/WCNC55385.2023.10118623

Spectrum occupancy prediction based on adaptive recurrent neural networks

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Author: Umebayashi, Kenta1; Kasahara, Yoshiki2; Iwata, Hiroki3;
Organizations: 1Tokyo University of Agriculture and Technology, Tokyo, Japan
2KDDI Corporation, Tokyo, Japan
3Hitachi Kokusai Electric Inc., Tokyo, Japan
4University of Helsinki, Helsinki, Finland
5University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-10-10


This paper investigates the prediction of spectrum usage by wireless local area networks based on recurrent neural networks (RNNs). The prediction results can be used to enhance the spectrum efficiency in dynamic spectrum sharing, and accuracy of spectrum sensing in cognitive radio networks. Observed time series of duty cycle (DC), which indicates the spectrum usage trend, is utilized to predict the future DC by the RNN. At first, we reveal a drawback of prediction by a single RNN, and this approach is denoted by a conventional approach in this paper. Specifically, the prediction results may have a significant biased error if the observed DCs are biased to either high or low values. For this problem, we propose the DC prediction with two RNNs and each of them designed for high DC case and low DC case, respectively. The prediction algorithm at first identifies the state of current DC either high or low. Then, the RNN for the identified state is performed for an accurate DC prediction. Numerical evaluations based on comprehensive measurement experiments of spectrum usage have presented that the proposed DC prediction can improve the accuracy of DC prediction compared to the conventional approach.

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Series: IEEE Wireless Communications and Networking Conference
ISSN: 1525-3511
ISSN-E: 1558-2612
ISSN-L: 1525-3511
ISBN: 978-1-6654-9122-8
ISBN Print: 978-1-6654-9123-5
Pages: 1 - 6
DOI: 10.1109/WCNC55385.2023.10118623
Host publication: 2023 IEEE Wireless Communications and Networking Conference (WCNC)
Conference: IEEE Wireless Communications and Networking Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work of Kenta Umebayashi was supported by the JSPS KAKENHI Grant Number JP18KK0109.
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